import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans, AgglomerativeClustering
from sklearn.preprocessing import StandardScaler
import plotly.express as px
from sklearn import preprocessing
from sklearn.metrics import silhouette_score
from scipy.cluster.hierarchy import dendrogram, linkage
import scipy.cluster.hierarchy as shc
from sklearn.decomposition import PCA

df = pd.read_csv("heart_cleveland_upload.csv")

df

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal	condition
0	69	1	0	160	234	1	2	131	0	0.1	1	1	0	0
1	69	0	0	140	239	0	0	151	0	1.8	0	2	0	0
2	66	0	0	150	226	0	0	114	0	2.6	2	0	0	0
3	65	1	0	138	282	1	2	174	0	1.4	1	1	0	1
4	64	1	0	110	211	0	2	144	1	1.8	1	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
292	40	1	3	152	223	0	0	181	0	0.0	0	0	2	1
293	39	1	3	118	219	0	0	140	0	1.2	1	0	2	1
294	35	1	3	120	198	0	0	130	1	1.6	1	0	2	1
295	35	0	3	138	183	0	0	182	0	1.4	0	0	0	0
296	35	1	3	126	282	0	2	156	1	0.0	0	0	2	1

297 rows × 14 columns

df.describe

<bound method NDFrame.describe of      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \
0     69    1   0       160   234    1        2      131      0      0.1   
1     69    0   0       140   239    0        0      151      0      1.8   
2     66    0   0       150   226    0        0      114      0      2.6   
3     65    1   0       138   282    1        2      174      0      1.4   
4     64    1   0       110   211    0        2      144      1      1.8   
..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   
292   40    1   3       152   223    0        0      181      0      0.0   
293   39    1   3       118   219    0        0      140      0      1.2   
294   35    1   3       120   198    0        0      130      1      1.6   
295   35    0   3       138   183    0        0      182      0      1.4   
296   35    1   3       126   282    0        2      156      1      0.0   

     slope  ca  thal  condition  
0        1   1     0          0  
1        0   2     0          0  
2        2   0     0          0  
3        1   1     0          1  
4        1   0     0          0  
..     ...  ..   ...        ...  
292      0   0     2          1  
293      1   0     2          1  
294      1   0     2          1  
295      0   0     0          0  
296      0   0     2          1  

[297 rows x 14 columns]>

Data Visualization

corr = df.corr()
mask = np.triu(np.ones_like(corr, dtype=bool))
plt.figure(figsize=(9, 6))
sns.heatmap(corr, mask=mask)
plt.title('Correlation Heatmap', fontsize=15)
plt.show()

Pre-Processing

df.isna().sum()

age          0
sex          0
cp           0
trestbps     0
chol         0
fbs          0
restecg      0
thalach      0
exang        0
oldpeak      0
slope        0
ca           0
thal         0
condition    0
dtype: int64

df = df.drop(columns = ['condition'])
##standardization
standardized_df = pd.DataFrame(preprocessing.StandardScaler().fit_transform(df),columns = df.columns)
standardized_df.head()

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal
0	1.600302	0.691095	-2.240629	1.596266	-0.257179	2.430427	1.010199	-0.812095	-0.696419	-0.820813	0.643781	0.344824	-0.874292
1	1.600302	-1.446980	-2.240629	0.468418	-0.160859	-0.411450	-1.003419	0.061157	-0.696419	0.639470	-0.976583	1.411625	-0.874292
2	1.268242	-1.446980	-2.240629	1.032342	-0.411292	-0.411450	-1.003419	-1.554358	-0.696419	1.326662	2.264145	-0.721976	-0.874292
3	1.157555	0.691095	-2.240629	0.355633	0.667499	2.430427	1.010199	1.065396	-0.696419	0.295874	0.643781	0.344824	-0.874292
4	1.046868	0.691095	-2.240629	-1.223355	-0.700254	-0.411450	1.010199	-0.244481	1.435916	0.639470	0.643781	-0.721976	-0.874292

corr = df.corr()
print(corr)

               age       sex        cp  trestbps      chol       fbs  \
age       1.000000 -0.092399  0.110471  0.290476  0.202644  0.132062   
sex      -0.092399  1.000000  0.008908 -0.066340 -0.198089  0.038850   
cp        0.110471  0.008908  1.000000 -0.036980  0.072088 -0.057663   
trestbps  0.290476 -0.066340 -0.036980  1.000000  0.131536  0.180860   
chol      0.202644 -0.198089  0.072088  0.131536  1.000000  0.012708   
fbs       0.132062  0.038850 -0.057663  0.180860  0.012708  1.000000   
restecg   0.149917  0.033897  0.063905  0.149242  0.165046  0.068831   
thalach  -0.394563 -0.060496 -0.339308 -0.049108 -0.000075 -0.007842   
exang     0.096489  0.143581  0.377525  0.066691  0.059339 -0.000893   
oldpeak   0.197123  0.106567  0.203244  0.191243  0.038596  0.008311   
slope     0.159405  0.033345  0.151079  0.121172 -0.009215  0.047819   
ca        0.362210  0.091925  0.235644  0.097954  0.115945  0.152086   
thal      0.120795  0.370556  0.266275  0.130612  0.023441  0.051038   

           restecg   thalach     exang   oldpeak     slope        ca      thal  
age       0.149917 -0.394563  0.096489  0.197123  0.159405  0.362210  0.120795  
sex       0.033897 -0.060496  0.143581  0.106567  0.033345  0.091925  0.370556  
cp        0.063905 -0.339308  0.377525  0.203244  0.151079  0.235644  0.266275  
trestbps  0.149242 -0.049108  0.066691  0.191243  0.121172  0.097954  0.130612  
chol      0.165046 -0.000075  0.059339  0.038596 -0.009215  0.115945  0.023441  
fbs       0.068831 -0.007842 -0.000893  0.008311  0.047819  0.152086  0.051038  
restecg   1.000000 -0.072290  0.081874  0.113726  0.135141  0.129021  0.013612  
thalach  -0.072290  1.000000 -0.384368 -0.347640 -0.389307 -0.268727 -0.258386  
exang     0.081874 -0.384368  1.000000  0.289310  0.250572  0.148232  0.323268  
oldpeak   0.113726 -0.347640  0.289310  1.000000  0.579037  0.294452  0.336809  
slope     0.135141 -0.389307  0.250572  0.579037  1.000000  0.109761  0.260096  
ca        0.129021 -0.268727  0.148232  0.294452  0.109761  1.000000  0.248825  
thal      0.013612 -0.258386  0.323268  0.336809  0.260096  0.248825  1.000000  

threshold = 0.5

high_corr = corr[(corr.abs() > threshold) & (corr != 1.0)]

plt.figure(figsize=(10, 8))
sns.heatmap(high_corr, annot=True, cmap="coolwarm", mask=high_corr.isnull())
sns.set_style('dark')
plt.title("Heatmap of High Correlations")
plt.show()

pca = PCA(n_components=0.95)
pca.fit(standardized_df)

plt.plot(pca.explained_variance_ratio_) 

plt.xlabel('Component Number')
plt.ylabel('Explained Variance Ratio')
plt.title('Explained Variance By Component')

plt.show()

pca = PCA(n_components = 3, random_state = 17) 
pca_df = pd.DataFrame(pca.fit_transform(standardized_df))
pca_df.shape

(297, 3)

K-Means Clustering

inertias = []

for i in range(1,11):
    kmeans = KMeans(n_clusters=i, random_state=5)
    clusters = kmeans.fit_predict(pca_df)
    inertias.append(kmeans.inertia_)

plt.plot(range(1,11), inertias, marker='o')
plt.title('Elbow method')
plt.xlabel('Number of clusters')
plt.ylabel('Inertia')
plt.show()

silhouette_scores = []

for i in range(2,11):
    kmeans = KMeans(n_clusters=i, random_state=42)
    clusters = kmeans.fit_predict(pca_df)
    silhouette_scores.append(silhouette_score(pca_df, clusters))

plt.plot(range(2,11), silhouette_scores, marker='x')
plt.title('Silhouette Score')
plt.xlabel('Number of clusters')
plt.ylabel('Score')
plt.show()

kmeans = KMeans(n_clusters=3, random_state=42)
kmeans.fit(pca_df)
standardized_df["Clusters"] = kmeans.predict(pca_df)

standardized_df['Clusters'].value_counts()

Clusters
2    117
1     97
0     83
Name: count, dtype: int64

subset = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal']
fig, axs = plt.subplots(3, 3, figsize=(15, 15))

axs = axs.flatten()

i = 0

for column in subset:
    sns.boxplot(data=standardized_df, y=column, x=standardized_df["Clusters"], ax = axs[i])
    axs[i].set_title(f'Box Plot of {column}')
    axs[i].set_xlabel('Clusters')
    axs[i].set_ylabel(column)

    i = i + 1

plt.tight_layout()
plt.show()

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Cell In[218], line 9
      6 i = 0
      8 for column in subset:
----> 9     sns.boxplot(data=standardized_df, y=column, x=standardized_df["Clusters"], ax = axs[i])
     10     axs[i].set_title(f'Box Plot of {column}')
     11     axs[i].set_xlabel('Clusters')

IndexError: index 9 is out of bounds for axis 0 with size 9

sns.pairplot(standardized_df, hue='Clusters', vars=subset)
plt.show()

kmeans = KMeans(n_clusters=4, random_state=42)
kmeans.fit(pca_df)
standardized_df["Clusters"] = kmeans.predict(pca_df)

standardized_df['Clusters'].value_counts()

Clusters
2    90
1    85
3    63
0    59
Name: count, dtype: int64

fig, axs = plt.subplots(3, 3, figsize=(15, 15))

axs = axs.flatten()

i = 0

for column in subset:
    sns.boxplot(data=standardized_df, y=column, x=standardized_df["Clusters"], ax = axs[i])
    axs[i].set_title(f'Box Plot of {column}')
    axs[i].set_xlabel('Clusters')
    axs[i].set_ylabel(column)

    i = i + 1

plt.tight_layout()
plt.show()

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Cell In[223], line 8
      5 i = 0
      7 for column in subset:
----> 8     sns.boxplot(data=standardized_df, y=column, x=standardized_df["Clusters"], ax = axs[i])
      9     axs[i].set_title(f'Box Plot of {column}')
     10     axs[i].set_xlabel('Clusters')

IndexError: index 9 is out of bounds for axis 0 with size 9

sns.pairplot(standardized_df, hue='Clusters', vars=subset)
plt.show()

plt.figure(figsize=(20, 7))
plt.title("Dendrogram")
dend = shc.dendrogram(shc.linkage(pca_df, method='average'),truncate_mode='level',p =10)
plt.show()

agglo = AgglomerativeClustering(n_clusters = 2, linkage = 'ward')
standardized_df['Clusters'] = agglo.fit_predict(pca_df)

standardized_df['Clusters'].value_counts()

Clusters
0    216
1     81
Name: count, dtype: int64

sns.pairplot(standardized_df, hue='Clusters', vars=subset)
plt.show()

fig, axs = plt.subplots(3, 3, figsize=(15, 15))

axs = axs.flatten()

i = 0

for column in subset:
    sns.boxplot(data=standardized_df, y=column, x=standardized_df["Clusters"], ax = axs[i])
    axs[i].set_title(f'Box Plot of {column}')
    axs[i].set_xlabel('Clusters')
    axs[i].set_ylabel(column)

    i = i + 1

plt.tight_layout()
plt.show()

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Cell In[233], line 8
      5 i = 0
      7 for column in subset:
----> 8     sns.boxplot(data=standardized_df, y=column, x=standardized_df["Clusters"], ax = axs[i])
      9     axs[i].set_title(f'Box Plot of {column}')
     10     axs[i].set_xlabel('Clusters')

IndexError: index 9 is out of bounds for axis 0 with size 9

subset = ['thalach', 'age']
sns.pairplot(data=standardized_df, vars=subset, hue='Clusters', diag_kind='kde', corner=True)
plt.show()

subset = ['thalach', 'chol']
sns.pairplot(data=standardized_df, vars=subset, hue='Clusters', diag_kind='kde', corner=True)
plt.show()